58 machine-learning-"https:" "https:" "https:" "https:" "https:" Fellowship research jobs at University of Oslo
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machine learning techniques to develop emulators for the theoretical predictions of various observables as function of cosmological parameters. The candidate will develop and use skills in topics such as
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, causal inference, and machine learning to study real-world treatment patterns, effectiveness, and safety of monoclonal antibodies (mAbs) used in autoimmune, inflammatory, and neurological diseases. Using
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environment for the training and development of PhD candidates and postdoctoral fellows, including individually tailored career development plans with formal supervision and project-based learning. Secondments
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environment spanning physics, developmental biology, advanced imaging, and machine learning. Colourbox via Unsplash Colourbox What skills are important in this role? The Faculty of Mathematics and Natural
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severity of cyber threats. Both defenders and attackers are now using Machine Learning (ML) and Artificial Intelligence (AI), therefore research is needed to investigate and design more advanced
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intelligence/machine learning skills. The candidate’s research proposal must be closely connected to the call and the research of NCEI. Excellent skills in written and oral English. Personal suitability and
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Science About the project This PhD project integrates pharmacoepidemiology, causal inference, and machine learning to study real-world treatment patterns, effectiveness, and safety of monoclonal antibodies
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the areas of stochastic analysis and computational methods towards machine learning with focus on risk-sensitive decision making and control. Techniques may include forward, backward stochastic differential
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groundwater/geochemical modelling software (e.g., MODFLOW, PHREEQC). Experience with laboratory analytical methods (e.g., chromatography, mass spectrometry). Familiarity with AI or machine learning applications
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of AI and in particular machine learning (ML). As today’s mainstream AI/ML workloads often resort to large-scale and energy-hungry supercomputers, it is necessary have a more critical look at how HPC